System and method of feature selection for text classification using subspace sampling

A computer-implemented method for classification, comprising:

• using an input/output device receiving a text represented by a plurality of features for classification;
• and
• using a processor device configured to perform:
• selecting a subset from the plurality of features by subspace sampling using a probability distribution over a plurality of features from a corpus of training texts, the probability distribution having a probability assigned to each of the plurality of features that is proportional to a square of Euclidean norms of a plurality of rows of a plurality of left singular vectors of a matrix of the plurality of features representing the corpus of training texts;
• classifying the text using the subset of the plurality of features;
• and
• outputting the classification of the text classified using the subset of the plurality of features selected by subspace sampling.

A non-transitory computer-readable medium having computer executable instructions for performing steps of:

• receiving a text represented by a plurality of features for classification;
• selecting a subset from the plurality of features by subspace sampling using a
• probability distribution over a plurality of features from a corpus of training texts, the probability distribution having a probability assigned to each of the plurality of features that is proportional to a square of Euclidean norms of a plurality of rows of a plurality of left singular vectors of a matrix of the plurality of features representing the corpus of training texts;
• classifying the text using the subset of the plurality of features;
• storing a plurality of texts classified using the plurality of features selected by subspace sampling into a plurality of classes;
• and
• outputting the classification of the text classified using the subset of the plurality of features selected by subspace sampling.